Utilizing “You only look once” (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the backbone are diversified through CSPDarkNet-53. Feature maps are extracted using Spatial Pyramid Pooling and a Path Aggregation Network in the neck part. The head part aggregates and generates the final output. All bounding boxes by the YOLO v4 are mapped onto the 3D reconstructed bone images after being resized to match the same region as shown in the 2D CT images. The YOLO v4-based AI model was evaluated through precision–recall (PR) curves and the intersection over union (IoU). Our proposed system facilitated an intuitive display of the fractured area through a distinctive red mask overlaid on the 3D reconstructed bone images. The high average precision values (>0.60) were reported as 0.71 and 0.81 from the PR curves of the tibia and elbow, respectively. The IoU values were calculated as 0.6327 (tibia) and 0.6638 (elbow). When utilized by orthopedic surgeons in real clinical scenarios, this AI-powered 3D diagnosis support system could enable a quick and accurate trauma diagnosis.

Orthopaedic surgeons need to correctly identify bone fragments using 2D/3D CT images before trauma surgery. Advances in deep learning technology provide good insights into trauma surgery over manual diagnosis. This study demonstrates the application of the DeepLab v3+ -based deep learning model for the automatic segmentation of fragments of the fractured tibia and fibula from CT images and the results of the evaluation of the performance of the automatic segmentation. The deep learning model, which was trained using over 11 million images, showed good performance with a global accuracy of 98.92%, a weighted intersection over the union of 0.9841, and a mean boundary F1 score of 0.8921. Moreover, deep learning performed 5–8 times faster than the experts’ recognition performed manually, which is comparatively inefficient, with almost the same significance. This study will play an important role in preoperative surgical planning for trauma surgery with convenience and speed.

If reduction images of fractures can be provided in advance with artificial-intelligence (AI)-based technology, it can assist with preoperative surgical planning. Recently, we developed the AI-based preoperative virtual reduction model for orthopedic trauma, which can provide an automatic segmentation and reduction of fractured fragments. The purpose of this study was to validate a quality of reduction model of Neer 3- or 4-part proximal humerus fractures established by AI-based technology. To develop the AI-based preoperative virtual reduction model, deep learning performed the segmentation of fracture fragments, and a Monte Carlo simulation completed the virtual reduction to determine the best model. A total of 20 pre/postoperative three-dimensional computed tomography (CT) scans of proximal humerus fracture were prepared. The preoperative CT scans were employed as the input of AI-based automated reduction (AI-R) to deduce the reduction models of fracture fragments, meanwhile, the manual reduction (MR) was conducted using the same CT images. Dice similarity coefficient (DSC) and intersection over union (IoU) between the reduction model from the AI-R/MR and postoperative CT scans were evaluated. Working times were compared between the two groups. Clinical validity agreement (CVA) and reduction quality score (RQS) were investigated for clinical validation outcomes by 20 orthopedic surgeons. The mean DSC and IoU were better when using AI-R that when using MR (0.78 ± 0.13 vs. 0.69 ± 0.16, p < 0.001 and 0.65 ± 0.16 vs. 0.55 ± 0.18, p < 0.001, respectively). The working time of AI-R was, on average, 1.41% of that of MR. The mean CVA of all cases was 81%±14.7% (AI-R, 82.25%±14.27%; MR, 76.75%±14.17%, p = 0.06). The mean RQS was significantly higher when AI-R compared with MR was used (91.47 ± 1.12 vs. 89.30 ± 1.62, p = 0.045). The AI-based preoperative virtual reduction model showed good performance in the reduction model in proximal humerus fractures with faster working times. Beyond diagnosis, classification, and outcome prediction, the AI-based technology can change the paradigm of preoperative surgical planning in orthopedic surgery. Level IV.

Despite numerous attempts to correct forward head posture (FHP), definitive evidence-based screening and diagnostic methods remain elusive. This study proposes a preliminary diagnostic methodology for FHP, utilizing a noninvasive body angle measurement system as a screening test for FHP and incorporating radiological parameters for sagittal alignment. We enrolled 145 adolescents for FHP screening. The forward neck tilt angle (FNTA), defined as the angle between the vertical line and the line connecting the participant’s acromion and tragus, was measured using the POM-Checker (a noninvasive depth sensor-based body angle measurement system). A whole-spine standing lateral radiograph was obtained, and eight sagittal alignment parameters were measured. Statistical analyses of the association between the FNTA and eight sagittal alignment parameters were conducted. We used 70% of the participant data to establish a preliminary diagnostic model for FHP based on FNTA and each sagittal alignment parameter. The accuracy of the model was evaluated using the remaining 30% of the participant data. All radiological parameters of sagittal alignment showed weak statistical significance with respect to FNTA (best case: r = 0.16, p = 0.0500; cranial tilt). The proposed preliminary diagnostic model for FHP demonstrated 95.35% agreement. Notably, the model using FNTA without radiological parameters accurately identified (100%) participants who required radiographic scanning for FHP diagnosis. Owing to the weak statistical significance of the association between radiological parameters and external body angle, both factors must be considered for accurate FHP diagnosis. When a clear and severe angle variation is observed in an external body angle check, medical professionals should perform radiographic scanning for an accurate FHP diagnosis. In conclusion, FNTA assessment of FNTA through the proposed preliminary diagnostic model is a significant screening factor for selecting participants who must undergo radiographic scanning so that a diagnosis of FHP can be obtained.

Orthopedic surgeons use manual measurements, acetate templating, and dedicated software to determine the appropriate implant size for total knee arthroplasty (TKA). This study aimed to use deep learning (DL) to assist in deciding the femoral and tibial implant sizes without manual manipulation and to evaluate the clinical validity of the DL decision by comparing it with conventional manual procedures. Two types of DL were used to detect the femoral and tibial regions using the You Only Look Once algorithm model and to determine the implant size from the detected regions using convolutional neural network. An experienced surgeon predicted the implant size for 234 patient cases using manual procedures, and the DL model also predicted the implant sizes for the same cases. The exact accuracies of the surgeon’s template were 61.54% and 68.38% for predicting femoral and tibial implant sizes, respectively. Meanwhile, the proposed DL model reported exact accuracies of 89.32% and 90.60% for femoral and tibial implant sizes, respectively. The accuracy ± 1 levels of the surgeon and proposed DL model were 97.44% and 97.86%, respectively, for the femoral implant size and 98.72% for both the surgeon and proposed DL model for the tibial implant size. The observed differences and higher agreement levels achieved by the proposed DL model demonstrate its potential as a valuable tool in preoperative decision-making for TKA. By providing accurate predictions of implant size, the proposed DL model has the potential to optimize implant selection, leading to improved surgical outcomes.

Background: The purpose of this study was to demonstrate the usefulness of 3D image-based virtual reduction by validating the evaluation criteria according to guidelines suggested by the AO Surgery Reference. Methods: For this experiment, 19 intact radial ORTHObones (ORTHObones radius, 3B Scientific, Germany, Hamburg) without any fractures were prepared. All ORTHObones with six cortical marking holes (three points on the distal part and three points on the proximal part) were scanned using a CT scanner twice (before/after intentional fracture of the ORTHObone). After the virtual reduction of all 19 ORTHObones, accuracy evaluations using the four criteria (length variation, apposition variation, alignment variation, Rotation Variation) suggested in the AO Surgery Reference were performed. Results: The mean (M) length variation was 0.42 mm, with 0.01 mm standard deviation (SD). The M apposition variation was 0.48 mm, with 0.40 mm SD. The M AP angulation variation (for alignment variation) was 3.24°, with 2.95° SD. The M lateral angulation variation (for alignment variation) was 0.09°, with 0.13° SD. The M angle of axial rotation was 1.27° with SD: 1.19°. Conclusions: The method of accuracy evaluation used in this study can be helpful in establishing a reliable plan.